pip install openvaluation

from openvaluation import Engine

company = {
    "company": {"sector": "saas", "stage": "seed", "region": "us"},
    "financials": {"revenue": {"arr": 480_000}},
    "berkus": {"sound_idea": 1.0, "prototype": 1.0, "management_team": 0.8,
               "strategic_relationships": 0.4, "product_rollout": 0.6},
    "scorecard": {"management_team": 1.25, "opportunity_size": 1.4},
}

print(Engine().run_all(company, stage="seed").summary())

4 methods ran; median 4,420,000 USD (range 1,400,000–6,462,500)

  berkus                         1,900,000  [1,400,000 – 2,400,000]
  ev_arr                         3,840,000  [2,400,000 – 5,760,000]
  risk_factor_summation          5,000,000  [4,750,000 – 5,250,000]
  scorecard                      5,875,000  [5,287,500 – 6,462,500]

  2 methods could not run:
    vc_method: vc_method needs exit.value, exit.revenue (supply an exit value, or
      projected revenue at exit to apply a multiple to)
    first_chicago: first_chicago needs scenarios.success.probability, ...

result = Engine().run(company, "berkus")
print(result.explain())

berkus: 1,900,000 USD (range 1,400,000–2,400,000)

Steps
  1. Sound idea — basic value, product risk: 500,000  — rating 1.00
  2. Prototype — technology risk: 500,000  — rating 1.00
  3. Quality management team — execution risk: 400,000  — rating 0.80
  4. Strategic relationships — market risk: 200,000  — rating 0.40
  5. Product rollout or sales — production risk: 300,000  — rating 0.60
  6. Pre-money valuation: 1,900,000  — sum of five elements

Assumptions
  cap_per_element: 500000.0

Limitations
  - Berkus caps pre-revenue value and ignores market size, growth and financials.
  - Ratings are judgements, not measurements; this run capped at 2,500,000.
  - This company reports revenue; Berkus was designed for pre-revenue companies
    and a revenue-based method will usually say more.

Sources
  - Dave Berkus, 'The Berkus Method: Valuing an Early Stage Investment' (berkonomics.com)

report = Engine().readiness(company)

[m.method for m in report.ready]     # ['berkus', 'scorecard', 'risk_factor_summation', 'ev_arr']
report.unlocks()
# {'exit.value|exit.revenue': ('vc_method',),
#  'financials.ebitda': ('ev_ebitda',),
#  'financials.revenue.annual': ('ev_revenue',)}

from openvaluation import Berkus, RiskFactorSummation

Berkus(cap_per_element=300_000)          # a market where 500k is too rich
RiskFactorSummation(step=100_000)        # finer-grained risk adjustments

  - Benchmark figures are illustrative placeholders, not market data; replace
    StaticBenchmarks with a real source before relying on this figure

from openvaluation import Engine, Multiple, TableBenchmarks

benchmarks = TableBenchmarks(
    seed_valuations={"saas": 4_200_000},
    multiple_table={("saas", "ARR"): Multiple(4.1, 6.8, 11.2, basis="ARR",
                                              source="Our comp set", sample_size=180,
                                              as_of="2026-06-30")},
    rate_table={"seed": 0.5},
    citations=("Our comp set, n=180, June 2026",),
)

engine = Engine(benchmarks=benchmarks)

pip install "openvaluation[mcp]"

{"mcpServers": {"openvaluation": {"command": "openvaluation-mcp"}}}

from openvaluation.tools import check_readiness, value_company

check_readiness(company)   # plain dicts in, plain dicts out

openvaluation company.json                     # every applicable method
openvaluation company.json --readiness         # what can run, what is missing
openvaluation company.json --method berkus --explain
openvaluation company.json --json              # for piping onward
openvaluation --list-methods

{
  "company":    {"sector": "saas", "stage": "seed", "region": "us"},
  "financials": {"revenue": {"arr": 480000, "annual": 520000}, "ebitda": 90000},
  "product":    {"stage": "mvp"},
  "berkus":     {"sound_idea": 1.0, "prototype": 0.8},
  "scorecard":  {"management_team": 1.25, "opportunity_size": 1.4},
  "risk":       {"management": 2, "competition": -1},
  "exit":       {"revenue": 40000000, "years": 5, "dilution": 0.3},
  "funding":    {"round_size": 2000000},
  "scenarios":  {"success": {"value": 80000000, "probability": 0.15},
                 "base":    {"value": 15000000, "probability": 0.35},
                 "failure": {"value": 0,        "probability": 0.50}}
}

git clone https://github.com/yagebin79386/openvaluation
cd openvaluation
pip install -e ".[dev]"
pytest